I don't quite understand your question, but I think you want to know the mean difference between age1 of 1 and 3 (for example) for males. The third contrast will do this, but I show the first two so you can see the steps. estimate 'm age 1' int 1 sex 1 0 age1  1  sex*age1 1; estimate 'm age 3' int 1 sex 1 0 age1  3  sex*age1 3; estimate 'm, age 3-1               ' age1  2  sex*age1 2; Note the the last estimate is the difference between the first two (which give you means for age 1 and age 3, both for males only [the first level of sex]). WIth the difference, terms like the intercept and sex main effect cancel. By the way, there are only two levels to your sex*age1 interaction, so your estimate statements will not work. ... View more

THis was just an example. Search this website for interaction of continuous and categorical. There are several posts. ... View more

Search some old posts. This has come up several times in the past. Here is one place to start: ... View more

Too bad you don't have a newer version of SAS, because the LINES option and SLICE statement in GLIMMIX do exactly what you want! If you are in education or noncommercial activity, you can always install SAS University Edition (it is free),. and it is always the newest version. But that would take a while for you to learn about. With only 9.1, the table of mean differences you showed would have to be translated by you, by hand, into the letter (lines) display. You sort the means from high to low (or low to high), and then figure out which ones are different (different letters) and which ones are the same using the difference table. This can be tedious and take a while. You have an alternative that I think (?) should work in 9.1 (not absolutely sure). You can use the macro written by Hans-Peter Piepho. See the article here. You would need to download the macro and run it first (every time you launch SAS for the mean separation analysis). Then you follow the directions in the article to get the letter display. Note that you have to use the ODS output statement to save LSMEANS and mean differences from a MIXED run. With the interaction, you do slices of one factor at fixed values of the other.  Good luck.(By the way, the macro can also do things not possible with the LINES option in current versions of SAS, but that is not relevant for your application). http://agrobiol.sggw.waw.pl/~cbcs/articles/CBCS_7_1_2.pdf You do need PROC IML for the macro to work. Message was edited by: Larry Madden ... View more

Steve raises the important point. You have subsamples for the control, not replicated experimental units. This is different. I am not sure if Steve's code will work since there are subsamples only for one treatment (variance may not be estimable). An approach that would work would be to get the average of the subsamples for the control (for each block), and use that as the single observation for the control for each block. ... View more

Remember, don't try comparing your results to the ANOVA output. The latter will be wrong for several parts of the mean comparisons! I am not sure what you are asking; it is fine to use the dunnett method, but be careful, SAS is deciding on the level of the factors to use as the control (the default is the first level of A and the first level of B). This can be altered with diff= option. Not sure what you mean about letters. I am guessing you want to see a table of means followed by mean separation letters. This is only done when all combinations of means are compared, not just with the control. You can do this with GLIMMIX, not MIXED, using the LINES option on the LSMEANS statement (as long as you use an adjustment that gives all mean comparisons). The code below does this. Note that you have a significant interaction, and the multiple comparison table is for theinteractions. However, most would argue that you would want to restrict comparisons to all levels of A at each level of B, or all levels of B at each level of A (the so-called simple effects). To get this, add the line: slice A*B / sliceby=B adjust=simulate lines; to get the simple effects for each level of B (as an example). proc glimmix;    class A B Block;    model Y = A B A*B;    random Block A*Block;    lsmeans A|B / adjust=simulate lines;   run; ... View more

Never, ever, use PROC ANOVA or PROC GLM for split plots, Standard errors will be wrong. Use MIXED or GLIMMIX. The later are much easier anyway: by specifying random effects, the procedure knows how to do each appropriate test. Here is code assuming that A is the whole plot and B is the subplot. proc glimmix data =  ; class block A B; model yield = A|B; random block A*block lsmeans A B A*B / diff adjust=simulate; run; Duncan multiple range is definitely out of favor, and can't be done with MIXED or GLIMMIX. I personally recommend adjust=simulate (fewer unrealistic assumptions). ... View more

You don't need to do anything different. ... View more

This can easily happen with fixed-effect models. ... View more

They could all be the same for SOME models. In general, however, they will differ substantially. You didn't say what model you are using. What random effects, etc. Only KR affects standard errors and fixed effect estimates. ... View more

Check out proc logistic, with ROC statement. http://www.sas.com/offices/NA/canada/downloads/presentations/BC-Fall2010/Proc.pdf ... View more